Ects : 3

Enseignant responsable :

• Etienne DECENCIERE

Volume horaire : 24

Description du contenu de l'enseignement :

What you will learn in this class?

• Intro and Course Overview
• Supervised Learning behaviors
• Principles of Reinforcement Learning
• Policy Gradients
• Actor-Critic Algorithms (A2C, A3C and Soft AC)
• Value Function Methods
• Deep RL with Q-functions
• Advanced Policy Gradient (DDPG, Twin Delayed DDPG)
• Trust Region & Proximal Policy Optimization (TRPO, PPO)
• Optimal Control and Planning
• Model-Based Reinforcement Learning
• Model-Based Policy Learning
• Exploration and Stochastic Bandit in RL
• Exploration with Curiosity and Imagination
• Offline RL and Generalization issues
• Offline RL and Policy constraints

 

Why you should choose this course about DRL?

• DRL Is a very promising type of learning as it does not need to know the solution
• DRL Only needs the rules and good rewards
• DRL Combines the best aspects of deep learning and reinforcement learning.
• DRL has achieved impressive results in games, robotic, finance and many more fields

 

References

• Bertsekas, Dynamic Programming and Optimal Control, Vols I and II
• Goodfellow, Bengio, Deep Learning
• Powell, Approximate Dynamic Programming
• Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming
• Sutton & Barto, Reinforcement Learning: An Introduction
• Szepesvari, Algorithms for Reinforcement Learning

Compétence à acquérir :

What you will acquire in this class?

• Understand principles of Deep Reinforcement Learning (DRL)
• Know main DRL algorithms
• Get some intuition about what DRL is good and not good at?
• Program DRL algorithms